{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Classification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A logistic regression analyzes the relationship between a binary target variable and its predictor variables to estimate the probability of the dependent variable taking the value 1. In the presence of temporal data where observations along time aren't independent, the errors of the model will be correlated through time and incorporating autoregressive features or lags can capture temporal dependencies and enhance the predictive power of logistic regression.\n",
    "\n",
    "<br>NHITS's inputs are static exogenous $\\mathbf{x}^{(s)}$, historic exogenous $\\mathbf{x}^{(h)}_{[:t]}$, exogenous available at the time of the prediction $\\mathbf{x}^{(f)}_{[:t+H]}$ and autorregresive features $\\mathbf{y}_{[:t]}$, each of these inputs is further decomposed into categorical and continuous. The network uses a multi-quantile regression to model the following conditional probability:$$\\mathbb{P}(\\mathbf{y}_{[t+1:t+H]}|\\;\\mathbf{y}_{[:t]},\\; \\mathbf{x}^{(h)}_{[:t]},\\; \\mathbf{x}^{(f)}_{[:t+H]},\\; \\mathbf{x}^{(s)})$$\n",
    "\n",
    "In this notebook we show how to fit NeuralForecast methods for binary sequences regression. We will:\n",
    "- Installing NeuralForecast.\n",
    "- Loading binary sequence data.\n",
    "- Fit and predict temporal classifiers.\n",
    "- Plot and evaluate predictions.\n",
    "\n",
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Temporal_Classifiers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%capture\n",
    "#!pip install neuralforecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import MLP, NHITS, LSTM\n",
    "from neuralforecast.losses.pytorch import DistributionLoss, Accuracy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading Binary Sequence Data\n",
    "\n",
    "The `core.NeuralForecast` class contains shared, `fit`, `predict` and other methods that take as inputs pandas DataFrames with columns `['unique_id', 'ds', 'y']`, where `unique_id` identifies individual time series from the dataset, `ds` is the date, and `y` is the target binary variable. \n",
    "\n",
    "In this motivation example we convert 8x8 digits images into 64-length sequences and define a classification problem, to identify when the pixels surpass certain threshold.\n",
    "We declare a pandas dataframe in long format, to match NeuralForecast's inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "digits = datasets.load_digits()\n",
    "images = digits.images[:100]\n",
    "\n",
    "plt.imshow(images[0,:,:], cmap=plt.cm.gray, \n",
    "           vmax=16, interpolation=\"nearest\")\n",
    "\n",
    "pixels = np.reshape(images, (len(images), 64))\n",
    "ytarget = (pixels > 10) * 1\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(pixels[10])\n",
    "ax2.plot(ytarget[10], color='purple')\n",
    "ax1.set_xlabel('Pixel index')\n",
    "ax1.set_ylabel('Pixel value')\n",
    "ax2.set_ylabel('Pixel threshold', color='purple')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1910</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1912</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1913</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1914</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6395</th>\n",
       "      <td>99</td>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>99</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6397</th>\n",
       "      <td>99</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6398</th>\n",
       "      <td>99</td>\n",
       "      <td>1972</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6399</th>\n",
       "      <td>99</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6400 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    ds  y  pixels\n",
       "0             0  1910  0     0.0\n",
       "1             0  1911  0     0.0\n",
       "2             0  1912  0     5.0\n",
       "3             0  1913  1    13.0\n",
       "4             0  1914  0     9.0\n",
       "...         ...   ... ..     ...\n",
       "6395         99  1969  1    14.0\n",
       "6396         99  1970  1    16.0\n",
       "6397         99  1971  0     3.0\n",
       "6398         99  1972  0     0.0\n",
       "6399         99  1973  0     0.0\n",
       "\n",
       "[6400 rows x 4 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We flat the images and create an input dataframe\n",
    "# with 'unique_id' series identifier and 'ds' time stamp identifier.\n",
    "Y_df = pd.DataFrame.from_dict({\n",
    "            'unique_id': np.repeat(np.arange(100), 64),\n",
    "            'ds': np.tile(np.arange(64)+1910, 100),\n",
    "            'y': ytarget.flatten(), 'pixels': pixels.flatten()})\n",
    "Y_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fit and predict temporal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `NeuralForecast.fit` method you can train a set of models to your dataset. You can define the forecasting `horizon` (12 in this example), and modify the hyperparameters of the model. For example, for the `NHITS` we changed the default hidden size for both encoder and decoders.\n",
    "\n",
    "See the `NHITS` and `MLP` [model documentation](https://nixtla.github.io/neuralforecast/models.mlp.html).\n",
    "\n",
    ":::{.callout-warning}\n",
    "For the moment Recurrent-based model family is not available to operate with Bernoulli distribution output.\n",
    "This affects the following methods `LSTM`, `GRU`, `DilatedRNN`, and `TCN`. This feature is work in progress.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1\n",
      "Global seed set to 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 124: 100%|██████████| 4/4 [00:00<00:00, 50.22it/s, v_num=35, train_loss_step=0.260, train_loss_epoch=0.331]\n",
      "Predicting DataLoader 0: 100%|██████████| 4/4 [00:00<00:00, 37.07it/s]\n",
      "Epoch 124: 100%|██████████| 4/4 [00:00<00:00,  5.34it/s, v_num=37, train_loss_step=0.179, train_loss_epoch=0.180]\n",
      "Predicting DataLoader 0: 100%|██████████| 4/4 [00:00<00:00, 49.74it/s]\n"
     ]
    }
   ],
   "source": [
    "# %%capture\n",
    "horizon = 12\n",
    "\n",
    "# Try different hyperparmeters to improve accuracy.\n",
    "models = [MLP(h=horizon,                           # Forecast horizon\n",
    "              input_size=2 * horizon,              # Length of input sequence\n",
    "              loss=DistributionLoss('Bernoulli'),  # Binary classification loss\n",
    "              valid_loss=Accuracy(),               # Accuracy validation signal\n",
    "              max_steps=500,                       # Number of steps to train\n",
    "              scaler_type='standard',              # Type of scaler to normalize data\n",
    "              hidden_size=64,                      # Defines the size of the hidden state of the LSTM\n",
    "              #early_stop_patience_steps=2,         # Early stopping regularization patience\n",
    "              val_check_steps=10,                  # Frequency of validation signal (affects early stopping)\n",
    "              ),\n",
    "          NHITS(h=horizon,                          # Forecast horizon\n",
    "                input_size=2 * horizon,             # Length of input sequence\n",
    "                loss=DistributionLoss('Bernoulli'), # Binary classification loss\n",
    "                valid_loss=Accuracy(),              # Accuracy validation signal                \n",
    "                max_steps=500,                      # Number of steps to train\n",
    "                n_freq_downsample=[2, 1, 1],        # Downsampling factors for each stack output\n",
    "                #early_stop_patience_steps=2,        # Early stopping regularization patience\n",
    "                val_check_steps=10,                 # Frequency of validation signal (affects early stopping)\n",
    "                )             \n",
    "          ]\n",
    "nf = NeuralForecast(models=models, freq='Y')\n",
    "Y_hat_df = nf.cross_validation(df=Y_df, n_windows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>MLP</th>\n",
       "      <th>MLP-median</th>\n",
       "      <th>MLP-lo-90</th>\n",
       "      <th>MLP-lo-80</th>\n",
       "      <th>MLP-hi-80</th>\n",
       "      <th>MLP-hi-90</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>NHITS-median</th>\n",
       "      <th>NHITS-lo-90</th>\n",
       "      <th>NHITS-lo-80</th>\n",
       "      <th>NHITS-hi-80</th>\n",
       "      <th>NHITS-hi-90</th>\n",
       "      <th>y</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1962</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.754</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.955</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1964</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1965</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>99</td>\n",
       "      <td>1969</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.817</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>99</td>\n",
       "      <td>1970</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.587</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>99</td>\n",
       "      <td>1971</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>99</td>\n",
       "      <td>1972</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>99</td>\n",
       "      <td>1973</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    ds  cutoff    MLP  MLP-median  MLP-lo-90  MLP-lo-80  \\\n",
       "0             0  1962    1961  0.190         0.0        0.0        0.0   \n",
       "1             0  1963    1961  0.754         1.0        0.0        0.0   \n",
       "2             0  1964    1961  0.035         0.0        0.0        0.0   \n",
       "3             0  1965    1961  0.049         0.0        0.0        0.0   \n",
       "4             0  1966    1961  0.042         0.0        0.0        0.0   \n",
       "...         ...   ...     ...    ...         ...        ...        ...   \n",
       "1195         99  1969    1961  0.484         0.0        0.0        0.0   \n",
       "1196         99  1970    1961  0.587         1.0        0.0        0.0   \n",
       "1197         99  1971    1961  0.336         0.0        0.0        0.0   \n",
       "1198         99  1972    1961  0.046         0.0        0.0        0.0   \n",
       "1199         99  1973    1961  0.001         0.0        0.0        0.0   \n",
       "\n",
       "      MLP-hi-80  MLP-hi-90  NHITS  NHITS-median  NHITS-lo-90  NHITS-lo-80  \\\n",
       "0           1.0        1.0  0.422           0.0          0.0          0.0   \n",
       "1           1.0        1.0  0.955           1.0          1.0          1.0   \n",
       "2           0.0        0.0  0.000           0.0          0.0          0.0   \n",
       "3           0.0        0.0  0.015           0.0          0.0          0.0   \n",
       "4           0.0        0.0  0.000           0.0          0.0          0.0   \n",
       "...         ...        ...    ...           ...          ...          ...   \n",
       "1195        1.0        1.0  0.817           1.0          0.0          0.0   \n",
       "1196        1.0        1.0  0.495           0.0          0.0          0.0   \n",
       "1197        1.0        1.0  0.126           0.0          0.0          0.0   \n",
       "1198        0.0        0.0  0.000           0.0          0.0          0.0   \n",
       "1199        0.0        0.0  0.000           0.0          0.0          0.0   \n",
       "\n",
       "      NHITS-hi-80  NHITS-hi-90  y  pixels  \n",
       "0             1.0          1.0  0    10.0  \n",
       "1             1.0          1.0  1    12.0  \n",
       "2             0.0          0.0  0     0.0  \n",
       "3             0.0          0.0  0     0.0  \n",
       "4             0.0          0.0  0     0.0  \n",
       "...           ...          ... ..     ...  \n",
       "1195          1.0          1.0  1    14.0  \n",
       "1196          1.0          1.0  1    16.0  \n",
       "1197          1.0          1.0  0     3.0  \n",
       "1198          0.0          0.0  0     0.0  \n",
       "1199          0.0          0.0  0     0.0  \n",
       "\n",
       "[1200 rows x 17 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default NeuralForecast produces forecast intervals\n",
    "# In this case the lo-x and high-x levels represent the \n",
    "# low and high bounds of the prediction accumulating x% probability\n",
    "Y_hat_df = Y_hat_df.reset_index(drop=True)\n",
    "Y_hat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>MLP</th>\n",
       "      <th>MLP-median</th>\n",
       "      <th>MLP-lo-90</th>\n",
       "      <th>MLP-lo-80</th>\n",
       "      <th>MLP-hi-80</th>\n",
       "      <th>MLP-hi-90</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>NHITS-median</th>\n",
       "      <th>NHITS-lo-90</th>\n",
       "      <th>NHITS-lo-80</th>\n",
       "      <th>NHITS-hi-80</th>\n",
       "      <th>NHITS-hi-90</th>\n",
       "      <th>y</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1962</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1964</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1965</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>99</td>\n",
       "      <td>1969</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>99</td>\n",
       "      <td>1970</td>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>99</td>\n",
       "      <td>1971</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>99</td>\n",
       "      <td>1972</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>99</td>\n",
       "      <td>1973</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    ds  cutoff  MLP  MLP-median  MLP-lo-90  MLP-lo-80  \\\n",
       "0             0  1962    1961    0         0.0        0.0        0.0   \n",
       "1             0  1963    1961    1         1.0        0.0        0.0   \n",
       "2             0  1964    1961    0         0.0        0.0        0.0   \n",
       "3             0  1965    1961    0         0.0        0.0        0.0   \n",
       "4             0  1966    1961    0         0.0        0.0        0.0   \n",
       "...         ...   ...     ...  ...         ...        ...        ...   \n",
       "1195         99  1969    1961    0         0.0        0.0        0.0   \n",
       "1196         99  1970    1961    1         1.0        0.0        0.0   \n",
       "1197         99  1971    1961    0         0.0        0.0        0.0   \n",
       "1198         99  1972    1961    0         0.0        0.0        0.0   \n",
       "1199         99  1973    1961    0         0.0        0.0        0.0   \n",
       "\n",
       "      MLP-hi-80  MLP-hi-90  NHITS  NHITS-median  NHITS-lo-90  NHITS-lo-80  \\\n",
       "0           1.0        1.0      0           0.0          0.0          0.0   \n",
       "1           1.0        1.0      1           1.0          1.0          1.0   \n",
       "2           0.0        0.0      0           0.0          0.0          0.0   \n",
       "3           0.0        0.0      0           0.0          0.0          0.0   \n",
       "4           0.0        0.0      0           0.0          0.0          0.0   \n",
       "...         ...        ...    ...           ...          ...          ...   \n",
       "1195        1.0        1.0      1           1.0          0.0          0.0   \n",
       "1196        1.0        1.0      0           0.0          0.0          0.0   \n",
       "1197        1.0        1.0      0           0.0          0.0          0.0   \n",
       "1198        0.0        0.0      0           0.0          0.0          0.0   \n",
       "1199        0.0        0.0      0           0.0          0.0          0.0   \n",
       "\n",
       "      NHITS-hi-80  NHITS-hi-90  y  pixels  \n",
       "0             1.0          1.0  0    10.0  \n",
       "1             1.0          1.0  1    12.0  \n",
       "2             0.0          0.0  0     0.0  \n",
       "3             0.0          0.0  0     0.0  \n",
       "4             0.0          0.0  0     0.0  \n",
       "...           ...          ... ..     ...  \n",
       "1195          1.0          1.0  1    14.0  \n",
       "1196          1.0          1.0  1    16.0  \n",
       "1197          1.0          1.0  0     3.0  \n",
       "1198          0.0          0.0  0     0.0  \n",
       "1199          0.0          0.0  0     0.0  \n",
       "\n",
       "[1200 rows x 17 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define classification threshold for final predictions\n",
    "# If (prob > threshold) -> 1\n",
    "Y_hat_df['NHITS'] = (Y_hat_df['NHITS'] > 0.5) * 1\n",
    "Y_hat_df['MLP'] = (Y_hat_df['MLP'] > 0.5) * 1\n",
    "Y_hat_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot and Evaluate Predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the forecasts of both models againts the real values.\n",
    "And evaluate the accuracy of the `MLP` and `NHITS` temporal classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = Y_hat_df[Y_hat_df.unique_id==10]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "plt.plot(plot_df.ds, plot_df.y, label='target signal')\n",
    "plt.plot(plot_df.ds, plot_df['MLP'] * 1.1, label='MLP prediction')\n",
    "plt.plot(plot_df.ds, plot_df['NHITS'] * .9, label='NHITS prediction')\n",
    "ax.set_title('Binary Sequence Forecast', fontsize=22)\n",
    "ax.set_ylabel('Pixel Threshold and Prediction', fontsize=20)\n",
    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Accuracy: 77.7%\n",
      "NHITS Accuracy: 78.1%\n"
     ]
    }
   ],
   "source": [
    "def accuracy(y, y_hat):\n",
    "    return np.mean(y==y_hat)\n",
    "\n",
    "mlp_acc = accuracy(y=Y_hat_df['y'], y_hat=Y_hat_df['MLP'])\n",
    "nhits_acc = accuracy(y=Y_hat_df['y'], y_hat=Y_hat_df['NHITS'])\n",
    "\n",
    "print(f'MLP Accuracy: {mlp_acc:.1%}')\n",
    "print(f'NHITS Accuracy: {nhits_acc:.1%}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- [Cox D. R. (1958). “The Regression Analysis of Binary Sequences.” Journal of the Royal Statistical Society B, 20(2), 215–242.](https://arxiv.org/abs/2201.12886)\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
